Assessment of Breeding Potential of Foxtail Millet Varieties Using a TOPSIS Model Constructed Based on Distinctness, Uniformity, and Stability Test Characteristics

Foxtail millet (Setaria italica) is an important cereal crop with rich nutritional value. Distinctness, Uniformity, and Stability (DUS) are the prerequisites for the application of new variety rights for foxtail millet. In this study, we investigated 32 DUS test characteristics of 183 foxtail millet resources, studied their artificial selection trends, and identified the varieties that conform to breeding trends. The results indicated significant differences in terms of the means, ranges, and coefficients of variation for each characteristic. A correlation analysis was performed to determine the correlations between various DUS characteristics. A principal component analysis was conducted on 31 test characteristics to determine their primary characteristics. By plotting PC1 and PC2, all the germplasm resources could be clearly distinguished. The trends in foxtail millet breeding were identified through a differential analysis of the DUS test characteristics between the landrace and cultivated varieties. Based on these breeding trends, the optimal solution types for multiple evaluation indicators were determined; the weight allocation was calculated; and a specific TOPSIS algorithm was designed to establish a comprehensive multi-criteria decision-making model. Using this model, the breeding potential of foxtail millet germplasm resources were ranked. These findings provided important reference for foxtail millet breeding in the future.


Introduction
Foxtail millet (Setaria italica) is a cereal crop in the Poaceae family, widely distributed in temperate regions where it is used for food or feed purposes [1].Foxtail millet has been reported to have originated in China [2].Because of its excellent characteristics such as a short growth cycle, strong adaptability, high yield, and drought resistance, foxtail millet has become a highly favored important cereal crop [3][4][5].Additionally, compared to other small grain crops, foxtail millet has high nutritional value.It contains unique nutrient components and is gradually becoming a model crop for plant genomic research [6][7][8].Significant differences are observed in the characteristics of various foxtail millet varieties because of factors such as planting environment and genetic diversity, posing great challenges to foxtail millet breeding.Therefore, it is urgent to accurately understand and explore the breeding trends of foxtail millet.
Before systematic breeding, foxtail millet varieties were local landraces adapted to specific regional growing conditions and gradually evolved through long-term human selection and cultivation from wild green foxtail grass (Setaria viridis) [9].Breeding foxtail millet is closely related to market demand and economic development.Over time, breeding objectives change with variations in the market, and millet varieties cultivated and Plants 2024, 13, 2102 2 of 15 promoted at different stages exhibit different characteristics.Analyzing and summarizing the trends and characteristics of millet varieties can provide important guidance for future breeding efforts.Numerous reports are available on the evolution of agronomic characteristics in various millet-planting regions.For example, in some studies on millet cultivation in certain regions, researchers have identified a series of millet varieties and observed an increasing trend in yield over time.Key factors such as reduced plant height, early maturity, and improved lodging resistance have played significant roles [10][11][12].Similar trends were observed for millet varieties in other regions, indicating that millet breeding is a continuous process of optimization and improvement [13][14][15].These studies provided valuable references for millet breeding and offered the scientific basis for millet cultivation and production.
The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm serves as a crucial multi-attribute decision-making tool in assessing the merits of a series of alternative solutions [16].This algorithm primarily determines the optimal solution based on the relative proximity of the alternative solutions to the ideal and negative ideal solutions.Using the TOPSIS algorithm, various alternative solutions can be quickly and accurately evaluated.Therefore, it is widely applied across various decision-making domains [17,18].Ali Bagherzadeh et al. used the TOPSIS method to examine the qualitative suitability of different irrigation strategies for wheat crops within the framework of the Food and Agriculture Organization [19].Similarly, Nayak et al. used the TOPSIS method to identify the most suitable application of rice husk for potential use in energy generation [20].Furthermore, Enqin Zheng et al. used the TOPSIS algorithm to assess the impact of humic acid on rice yield under various irrigation methods [21].It is evident that the TOPSIS method holds significant value in evaluating cultivation methods and selecting agricultural intervention measures.
Distinctness, Uniformity, and Stability (DUS) is a process of plant variety identification tests or indoor analytical tests and determines whether a tested variety is a new variety based on the test results of distinctiveness, uniformity, and stability.It provides reliable criteria for the protection of new plant varieties and plays a significant role in plant breeding and new variety protection.Numerous studies have applied DUS testing methods in breeding to ensure the identifiability, distinguishability, and specificity of new varieties; this is performed through comprehensive comparisons and identifications of the morphological and biological characteristics and molecular markers of new varieties, effectively protecting the intellectual property rights of new varieties.For example, Tao Chen et al. improved the statistical phenotypic characteristics for DUS testing through the DUS identification of 143 germplasms and selected excellent germplasm resources for oil tea breeding using the TOPSIS algorithm [22].Liyuan Wang et al. determined wheat breeding trends by studying the differences in the DUS testing characteristics of wheat in various regions.The application of DUS testing has played a positive role in promoting the sustainable development of breeding strategies [23].
This study comprehensively evaluated 32 phenotypic characteristics of 183 foxtail millet germplasm resources to assess the diversity of and variation in foxtail millet in DUS testing.Specifically, by comparing the performance of landrace and cultivated varieties in these tests, we analyzed the trends in foxtail millet breeding in detail.Ultimately, using the TOPSIS method for establishing a model for ranking, we identified superior varieties aligning with the current breeding trends.These findings provided not only specific guidance and recommendations for foxtail millet cultivation and breeding but also valuable insights to advance research and practical applications in the field of foxtail millet breeding.

Observation and Analysis of DUS Testing Characteristics
The 32 phenotypic characteristics of 183 foxtail millet varieties were observed and analyzed.The results revealed that various phenotypic characteristics exhibited varying frequency distributions (Figure 1).In the foxtail millet germplasm resources, only one Plants 2024, 13, 2102 3 of 15 type of endosperm (glutinous) was observed.Therefore, this characteristic was not further analyzed.Characteristics 1, 2, 4, and 16 exhibited two expression types.Characteristics 3, 5, and 27 had narrow and single-level distributions.Characteristics 14, 25, and 26 exhibited the widest distribution range, with nine expression levels.Table 1 presents the mean, standard deviation, CV, and H ′ for the 32 phenotypic characteristics.The CV reflects the dispersion of and variability in data, with a larger coefficient indicating greater variability.Among these characteristics, characteristics 5, 13, and 29 exhibited relatively high variability, with CVs of 50.31%, 50.52%, and 50.13% respectively.Characteristics 1, 2, and 16 exhibited relatively low variability, with CVs of 9.89%, 9.15%, and 3.72% respectively.The characteristics related to yield exhibited extensive variation, with significant differences in grading for the characteristics, such as the panicle length, panicle thickness, number of grains per panicle, and individual panicle weight.Shannon's diversity index reflects the diversity and evenness of individual distribution.A higher diversity index indicates a more even distribution of individual characteristics in the varieties.Characteristics 14,15,20,22,23,25, and 26 had diversity indexes > 1.5.Among them, characteristics 25 and 2 had the highest and lowest diversity indexes of 2.032 and 0.183, respectively.

Observation and Analysis of DUS Testing Characteristics
The 32 phenotypic characteristics of 183 foxtail millet varieties were observed and analyzed.The results revealed that various phenotypic characteristics exhibited varying frequency distributions (Figure 1).In the foxtail millet germplasm resources, only one type of endosperm (glutinous) was observed.Therefore, this characteristic was not further analyzed.Characteristics 1, 2, 4, and 16 exhibited two expression types.Characteristics 3, 5, and 27 had narrow and single-level distributions.Characteristics 14, 25, and 26 exhibited the widest distribution range, with nine expression levels.Table 1 presents the mean, standard deviation, CV, and Hʹ for the 32 phenotypic characteristics.The CV reflects the dispersion of and variability in data, with a larger coefficient indicating greater variability.Among these characteristics, characteristics 5, 13, and 29 exhibited relatively high variability, with CVs of 50.31%, 50.52%, and 50.13% respectively.Characteristics 1, 2, and 16 exhibited relatively low variability, with CVs of 9.89%, 9.15%, and 3.72% respectively.The characteristics related to yield exhibited extensive variation, with significant differences in grading for the characteristics, such as the panicle length, panicle thickness, number of grains per panicle, and individual panicle weight.Shannon's diversity index reflects the diversity and evenness of individual distribution.A higher diversity index indicates a more even distribution of individual characteristics in the varieties.Characteristics 14,15,20,22,23,25, and 26 had diversity indexes > 1.5.Among them, characteristics 25 and 2 had the highest and lowest diversity indexes of 2.032 and 0.183, respectively.

Correlation of Phenotypic Characteristics
A correlation analysis was conducted on 31 agronomic characteristics (Figure 2).The results revealed various patterns of correlation among the characteristics.The individual panicle weight was significantly correlated with yield-related characteristics, such as the panicle length, panicle thickness, panicle density, and grains per panicle.On an average, each characteristic was correlated with 10.4 other characteristics.Characteristic 16 was not correlated with any other characteristics, whereas character 17 was correlated with the maximum (19) characteristics.Among all the significant correlations, the largest significantly positive correlation (r = 0.75) was observed between characteristics and 5, whereas the largest significantly negative correlation (r = −0.5)was observed between characteristics 4 and 6.Apart from these two correlations, the absolute values of the correlation coefficients between other combinations were ≤0.5, indicating weak correlations.

Cluster Analysis
Based on the data of 32 phenotypic characteristics, the 183 foxtail millet varieties were classified into seven clusters (Figure 3).Cluster

Cluster Analysis
Based on the data of 32 phenotypic characteristics, the 183 foxtail millet varieties were classified into seven clusters (Figure 3).

Principal Component Analysis (PCA)
A PCA was conducted on 183 foxtail millet germplasm resources to identify their major characteristics (Table 2).Overall, 11 significant components were selected, which accounted for 80.79% of the total variance based on eigenvalues > 1.Among them, the first, fifth, and ninth principal components were primarily composed of characteristics related to the seedling stage of foxtail millet (characteristics 1, 2, 3, 4, and 5), referred to as the seedling factor.The second and fourth principal components mainly comprised charac-

Principal Component Analysis (PCA)
A PCA was conducted on 183 foxtail millet germplasm resources to identify their major characteristics (Table 2).Overall, 11 significant components were selected, which accounted for 80.79% of the total variance based on eigenvalues > 1.Among them, the first, fifth, and ninth principal components were primarily composed of characteristics related to the seedling stage of foxtail millet (characteristics 1, 2, 3, 4, and 5), referred to as the seedling factor.The second and fourth principal components mainly comprised characteristics related to the panicle of foxtail millet (characteristics 22, 23, 24, 25, and 26), termed as the panicle factor.Characteristics 19 and 28 had a significant loading on the third principal component.The 6th, 7th, and 10th principal components were primarily loaded by individual characteristics (characteristics 16, 17, and 31) as the main negative loading factors.The eighth principal component mainly consisted of characteristics related to the color of the foxtail millet panicles (characteristics 9 and 13), known as the panicle color factor.The 11th principal component was primarily composed of characteristics 16 and 19 but with a lower loading.The projection of all the varieties onto PC1 and PC2 for plotting (Figure 4) demonstrated a clear separation between the landrace and cultivated varieties, indicating significant differences between them.

Analysis of Breeding Trends
Based on the results, the foxtail millet germplasm resources in this study were divided into two categories: landrace and cultivated varieties (52 and 131 varieties, respectively).A differential analysis of 32 DUS-tested characteristics of foxtail millet (Figure 5) was conducted to predict the current breeding trends in foxtail millet.The differential analysis between the landrace varieties and cultivated varieties revealed that 12 characteristics were not significantly different between the two, indicating that these characteristics are not major factors in the breeding process of foxtail millet.However, significant differences were observed in 20 characteristics between the landrace and cultivated varieties.Specifically, the cultivated varieties exhibited significant superiority over the landraces in characteristics 1 (p < 0.

Comprehensive Evaluation Using TOPSIS Algorithm
Based on the identified breeding trends in foxtail millet, the maximum values of the significantly increased characteristics and the minimum values of the significantly decreased characteristics were defined as the positive and negative ideal solutions, respectively.Each characteristic was given equal weight, and a comprehensive multi-criteria decision-making model was established using the TOPSIS algorithm to assess the breeding potential of the seed resources, ranking the landrace and cultivated varieties (Table 3).After computation and analysis, the top 10 varieties were selected in terms of breeding potential.

Comprehensive Evaluation Using TOPSIS Algorithm
Based on the identified breeding trends in foxtail millet, the maximum values of the significantly increased characteristics and the minimum values of the significantly decreased characteristics were defined as the positive and negative ideal solutions, respectively.Each characteristic was given equal weight, and a comprehensive multi-criteria decision-making model was established using the TOPSIS algorithm to assess the breeding potential of the seed resources, ranking the landrace and cultivated varieties (Table 3).After computation and analysis, the top 10 varieties were selected in terms of breeding potential.They were Changnong 41, Jinfen 117, Jinxuan 1012, Jinfen 119, Changgu K6, Dayoug 2, Jinfen 110, Jinfen 111, Jingug 21, and Huangjinggu 7.

Phenotypic Variation of Foxtail Millet Resources
The CV is an important indicator for assessing the degree of differences in phenotypic characteristics.It is significantly positively correlated with the degree of phenotypic differences and genetic diversity.This provides greater possibilities for utilizing phenotypic characteristics to identify the varieties and germplasms [23].The analysis of 32 DUS-tested characteristics of 183 foxtail millet germplasm resources revealed that various characteristics in foxtail millet germplasm resources have a high CV, indicating the presence of rich genetic diversity among foxtail millet germplasm resources.In terms of quantitative characteristics, the median and mean values of the 183 germplasm resources were essentially consistent, reflecting the representativeness of the study subjects.The H ′ values of the leaf, stem, and panicle characteristics were relatively high (1.078-2.032),indicating substantial genetic variation in these characteristics.In particular, leaf characteristics reflect the adaptability of plants to various environments and their self-regulation capacity in complex physiological environments; they are considered important indicators for plant science research [24].In contrast, the H ′ values of the grain and seedling characteristics in foxtail millet were lower (0.147-1.045), suggesting that foxtail millet is less affected during the seedling stage and exhibits less characteristic segregation.However, grain characteristics directly impact the yield and quality of foxtail millet and are important characteristics that breeders hope to modify.Nevertheless, due to the low diversity of foxtail millet grains, more constraints are presented for foxtail millet breeding.This emphasizes the importance of correctly identifying breeding trends in foxtail millet breeding.

Correlation Analysis and PCA
A significantly positive correlation was observed between the single panicle weight of foxtail millet and multiple characteristics, including the stem thickness, stem length, length of the second leaf, width of the second leaf, internode number, panicle posture, panicle length, panicle thickness, panicle density, and grain number per panicle.This is consistent with a previous study, indicating that the improvement in foxtail millet yield is related to multiple characteristics [25].This result is consistent with the source-sink theory [26], where the stem length and thickness of foxtail millet affect the permeability of the nutrients and water in the root system, whereas the increase in the length and width of the second leaf enhances the leaf area and thereby strengthens plant photosynthesis.Additionally, the increase in the panicle length, thickness, density, and grain number per panicle increases the grain yield of foxtail millet.Therefore, the enhancement of foxtail millet yield is influenced by multiple factors.By changing the characteristics related to yield toward the correct breeding trend, the yield of foxtail millet can be improved.
Furthermore, PCA is an effective method for reducing the dimensions of large datasets, enhancing interpretability, reducing information loss, and determining the characteristics that are most suitable and primarily responsible for the variation in the selected materials [27,28].In this study, the PCA confirmed that the first 11 components explained the majority of the variation, focusing on the characteristics, such as the leaf sheath color in seedlings, leaf posture, leaf hilum anthocyanin coloration, stem length, panicle length, panicle thickness, grain number per panicle, and single panicle weight.These results suggested that these characteristics are suitable for evaluating the genetic diversity of foxtail millet germplasm resources and can be used for phenotypic identification of foxtail millet germplasm resources.Through the analysis, the cultivated and landrace varieties could be clearly divided into two categories, with a certain degree of overlap.This further confirmed the transition from landrace varieties to modern cultivated varieties in the breeding history of foxtail millet.Because the history of foxtail millet breeding is not extensive, a wide range of phenotypic divergence could not be observed between the landrace and cultivated varieties in the breeding process, explaining the presence of the overlap in the PCA.

Analysis of Breeding Trends and Screening of Potential Varietal Resources
Before systematic breeding, foxtail millet varieties were local landraces adapted to specific regional growing conditions, gradually evolved through long-term human selection and cultivation from wild green foxtail grass (Setaria viridis) [9].In the breeding process of foxtail millet, foxtail millet germplasm resources, including landrace varieties and local cultivated varieties, are first collected from various regions and areas [29].These germplasm resources possess rich genetic variation and adaptability, playing a vital foundational role in foxtail millet breeding [30].Subsequently, through the evaluation and selection of these landrace varieties, superior individuals or populations with good agronomic and economic characteristics are selected.Further, by employing methods such as controlled hybridization, selection, and progeny screening, the yield, quality, and stress resistance of foxtail millet are gradually enhanced.
Our study determined the breeding trend of foxtail millet by comparing the differences in the DUS test characteristics between the landrace and cultivated varieties.The results indicated significant differences between these in terms of 20 characteristics, with 8 characteristics significantly increasing and 12 characteristics significantly decreasing during the breeding process.Previous studies reported that early cultivated foxtail millet varieties had long awns; however, most modern varieties have short awns [31].This change is attributed to the vulnerability of early foxtail millet cultivation to damage by birds; long-awned millet varieties are effectively protected against feeding by birds [32].With advances in modern technology for protection from birds and the decrease in bird populations due to environmental pollution, the length of the awns of foxtail millet gradually shortened.This is consistent with the findings of this study.Grains of cereal plants generally have long and narrow leaves.In this study, the length of the second leaf of foxtail millet gradually decreased, whereas the width of the second leaf increased.Additionally, the plant-to-leaf posture gradually exhibited an upward trend.This can be attributed to changes in the length-to-width ratio of the leaves, allowing them to meet the requirements of modern high-density cultivation, consistent with a previous study [33].Additionally, the increase in stem thickness enhances the plant's lodging resistance.Breeders optimize yield by increasing the grain weight of foxtail millet rather than the number of grains per panicle.Reducing the panicle length can make the wheat spikes more compact, reducing the impact of natural factors such as wind or birds on foxtail millet yield and increasing its recoverable rate.The code for the foxtail millet grain shape gradually increases, indicating a transition from ovate to spherical grain shapes.This results in an increase in individual grain volume, further explaining the increase in the thousand-grain weight of foxtail millet.
By constructing a TOPSIS model, this study ranked the breeding potential of foxtail millet germplasm resources, with those ranked higher exhibiting greater breeding potential consistent with the aforementioned breeding trends.Moreover, this model can be used to screen foxtail millet germplasm for subsequent DUS testing by selecting varieties with higher scores.The establishment of this model provided significant guidance for foxtail millet breeding, aiding in the selection of promising foxtail millet germplasm resources for further breeding work and accelerating the foxtail millet breeding process.

Plant Materials and Field Experiments
A total of 183 foxtail millet germplasm resources were collected for this study, comprising 52 landrace varieties and 131 cultivated varieties.The planting experiments were conducted in Xinzhou City, Shanxi Province, China (120 • 52 ′ E, 30 • 40 ′ N; altitude 791 m; annual precipitation 385-516 mm; and average annual temperature 5.0-9.8• C) in 2022 and 2023.The experiments were designed using a randomized complete block design with three replicates.Each variety was sown in late May, with a minimum of 300 plants per plot planted in 6 rows.Each plot measured 5 m in length and 2.4 m in width.The row spacing was set at 40 cm, and the plant spacing ranged from 7 to 10 cm.The soil at the experimental site was sandy loam.All the experiments were performed according to standard agricultural practices.Organic fertilizer and compound fertilizer were applied at 52,500 and 600 kg•hm −1 , respectively.After sowing, the experimental plots were irrigated twice: once during the seedling stage and again at the jointing stage using drip irrigation to ensure uniform water distribution.All the irrigation methods were followed as per standard agricultural practices.

Determination of Phenotypic Characteristics and Data Collection
In total, 32 characteristics were investigated as outlined in the foxtail millet DUS testing guidelines (Table 4), comprising 1 qualitative (QL), 14 pseudo-quantitative (PQ), and 17 quantitative (QN) characteristics.The characteristic observation methods included individual visual scoring (VS), population visual scoring (VG), individual measurements (MS), and population measurements (MG).In accordance with the guidelines, the corresponding codes were recorded for the visually scored characteristics.For each measured characteristic (e.g., the leaf length, leaf width, stem length, stem thickness, number of tillers per plant, panicle neck length, panicle length, panicle thickness, number of grains per panicle, individual panicle weight, grain yield, and thousand-grain weight), at least 20 typical plants were selected from each plot for individual measurement and recording.

Statistical Analysis
All the experiments were performed in triplicates.Based on a 2-year investigation and measurements, the mean of each characteristic was used for the statistical analysis.The qualitative and pseudo-qualitative characteristics were classified into 10 grades, 1 grade < X − 2S; 10 grades > X + 2S, with each grade interval being 0.5 s between 1 and 10 grades; X and s are the mean and standard deviation, respectively.The morphological diversity was evaluated using the frequency of characteristic dispersion and Shannon's diversity index (H ′ ).The minimum value (Min), maximum value (Max), mean, median, standard deviation (SD), coefficient of variation (CV; %), and H ′ of all the characteristics were measured.The CV for all the quantitative characteristics was calculated as CV¼ S = X, where S is the standard deviation and X is the mean.The H ′ for each characteristic was calculated using the following formula: H ′ = −Pi ln (Pi) (Pi is the proportion of the individual number of this characteristic in the total individual number).The IBM SPSS Statistics version 20.0 (SPSS Inc., Chicago, IL, USA) was used to estimate the correlation among all the quantitative characteristics with Pearson's correlation coefficient.A principal component analysis (PCA) was applied to determine the relationship among the individuals.Based on the breeding history of foxtail millet, the foxtail millet varieties were categorized into farmer varieties and breeding lines.The artificial selection trends were determined for the characteristics assessed in the DUS tests of the foxtail millet through a differential analysis between the farmer varieties and breeding lines.The results of the correlation analysis, principal component analysis, and differential analysis were visualized using the R package "ggplot2" (number of the software.3.3.6).This study employed the TOPSIS method for the multi-attribute decision analysis.Following the differential analysis revealing significant differences among the data, the dataset underwent thorough data cleaning to ensure consistency.In the analysis, all the evaluation criteria were equally weighted.If the newly bred varieties significantly exceeded the local landraces in a specific trait, the ideal solution was set as the maximum value; otherwise, it was set as the minimum value.Using TOPSIS, a model was constructed to evaluate each variety's comprehensive score based on the proximity score formula, thereby identifying the foxtail millet varieties with the greatest Plants 2024, 13, 2102 14 of 15 breeding potential.The process was performed using the R package "topsis" (number of the software.1.0.0).

Conclusions
In this study, we evaluated the phenotypic diversity and breeding trends of 183 foxtail millet germplasm resources based on 32 phenotypic characteristics.Our results revealed significant variability across multiple traits within the foxtail millet germplasm.Key traits relevant to foxtail millet breeding and germplasm identification were identified through correlation and PCA.Additionally, by analyzing the differential traits of landrace and cultivated varieties in DUS tests, we determined their breeding trends.Based on these trends, we established optimal solution types for multiple evaluation indicators, accordingly allocated weights, and developed a specific TOPSIS algorithm to construct a comprehensive multi-criteria decision-making model.This model was used to rank the breeding potential of foxtail millet germplasm resources.These findings will guide the expansion of the foxtail millet characterization system and optimization of DUS testing guidelines.Furthermore, this study provided a reference for the further utilization of and improvement in major traits in foxtail millet germplasm resources, laying a theoretical foundation for future breeding of new varieties.

Figure 1 .
Figure 1.Distribution of variation types of all DUS test characteristics in 183 millet varieties.

Figure 3 .
Figure 3. Cluster dendrogram of 183 millet varieties based on DUS testing characteristics.

Figure 4 .
Figure 4.The principal component analysis of the 183 millet varieties.Each point represents a variety, with the blue area indicating the clustering of cultivated varieties and the gray area representing the clustering of landrace varieties.

Figure 4 .
Figure 4.The principal component analysis of the 183 millet varieties.Each point represents a variety, with the blue area indicating the clustering of cultivated varieties and the gray area representing the clustering of landrace varieties.

Table 1 .
Variability and genetic diversity of all DUS test characters.Distribution of variation types of all DUS test characteristics in 183 millet varieties.

Table 1 .
Variability and genetic diversity of all DUS test characters.

Table 2 .
The principal component analysis of the 32 quantitative characters in the 183 Setaria italica accessions.

Table 2 .
The principal component analysis of the 32 quantitative characters in the 183 Setaria italica accessions.

Table 3 .
Comprehensive score and ranking of 183 Setaria italica accessions.

Table 4 .
The information on the wheat DUS testing characteristics used in this study.